association among income loss, financial strain and ......sep 18, 2020 · financial stressors with...

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Association among income loss, financial strain and depressive symptoms

during COVID-19: evidence from two longitudinal studies

Hertz-Palmor N1,2, Moore TM3,4, Gothelf D1,5,6, DiDomenico GE4, Dekel I1,5, Greenberg DM7, Brown

LA3, Matalon N1,5, Visoki E4, White LK4, Himes MH4, Schwartz-Lifshitz M1, Gross R1,5, Gur RC3,4,

Gur RE3,4,8, Pessach IM1,5*, Barzilay R3,4,8*

1 Sheba Medical Center, Ramat Gan, Israel 2 School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel 3 University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, PA, USA 4 Lifespan Brain Institute of the Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA 5 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel 6 Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel 7 Interdisciplinary Department of Social Sciences and Department of Music, Bar-Ilan University, Ramat Gan,

Israel 8 Children’s Hospital of Philadelphia Department of Child Adolescent Psychiatry and Behavioral Sciences,

Philadelphia, PA, USA

* Equal senior authors

Abstract

Background. The COVID-19 pandemic has major ramifications for global health and the economy,

with growing concerns about economic recession and implications for mental health. Here we

investigated the associations between COVID-19 pandemic-related income loss with financial strain

and mental health trajectories over a 1-month course.

Methods. Two independent studies were conducted in the U.S and in Israel at the beginning of the

outbreak (March-April 2020, T1; N = 4 171) and at a 1-month follow-up (T2; N = 1 559). Mixed-

effects models were applied to assess associations among COVID-19-related income loss, financial

strain, and pandemic-related worries about health, with anxiety and depression, controlling for

multiple covariates including pre-COVID-19 income.

Findings. In both studies, income loss and financial strain were associated with greater depressive

symptoms at T1, above and beyond T1 anxiety, worries about health, and pre-COVID-19 income.

Worsening of income loss was associated with exacerbation of depression at T2 in both studies.

Worsening of subjective financial strain was associated with exacerbation of depression at T2 in one

study (US).

Interpretation. Income loss and financial strain were uniquely associated with depressive symptoms

and the exacerbation of symptoms over time, above and beyond pandemic-related anxiety.

Considering the painful dilemma of lockdown versus reopening, with the tradeoff between public

health and economic wellbeing, our findings provide evidence that the economic impact of COVID-

19 has negative implications for mental health.

Funding. This study was supported by grants from the National Institute of Mental Health, the US-

Israel Binational Science Foundation, Foundation Dora and Kirsh Foundation.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted September 18, 2020. ; https://doi.org/10.1101/2020.09.15.20195339doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

https://doi.org/10.1101/2020.09.15.20195339

http://creativecommons.org/licenses/by-nc-nd/4.0/

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Introduction

The COVID-19 pandemic has major ramifications for global health1. To reduce virus spread,

many countries have ordered strict measures to impose social distancing2. While such

preventive measures are effective in reducing transmission, their economic costs are

overwhelming3–5. In April 2020, unemployment in the US reached 14·7%, a 10·3 percent

increase compared to the previous month, with 15·9 million newly unemployed workers. This

is the steepest one-month increase since 19483. Similar trends have been observed in other

areas of the world4. The World Bank has estimated that by the end of 2020, international

global gross domestic product (GDP) will decline by up to 5·2%, the worst global recession

in 80 years5. Behind the numbers, reduced GDP means greater unemployment, which is a

major risk factor for worsened mental health6. Unemployment increases psychological strain

as, besides the loss of income, it is accompanied with loss of social contact, status and sense

of competence7. Thus, the economic climate in mid-2020 poses an imminent threat to mental

health deterioration8, adding to the health-related stress invoked by the pandemic. Some

evidence from the 2007 economic crisis suggests that economic stress is specifically

associated with seeking mental health help due to depression9.

Recent findings indicate that the current COVID-19 crisis is no exception, with its economic

impact hitting hard on global mental health. In China, high levels of anxiety and depression

were associated with worries about income, job, study or inability to pay loans10. In a large

nationally representative study conducted in the UK, people who were unemployed or had no

income (e.g. were full-time students) during the pandemic were more distressed than those

employed. Furthermore, people who lost their job during the pandemic expressed greater

increase in distress than individuals who were already unemployed before COVID-1911. This

finding might reflect the link between the sharp decrease in financial security and

deterioration in mental health experienced by many workers; however, this study did not



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address the specificity of financial concerns to mental health beyond other concerns such as

COVID-19 health-related worries11. A study from the U.S reported higher rate of depressive

symptoms during the pandemic compared with pre-pandemic, highlighting low household

income and savings as risk factors for depression; however this study did not compare

financial stressors with health-related stressors12. In a study from Switzerland, the impact of

financial concerns on internalizing symptoms (depression, anxiety, self-injury and suicidal

ideation) among young adults (N = 768, mean age 22) exceeded that of health-related

concerns13. This finding is in line with a report that young people are less physically

threatened by the virus, but are more vulnerable to losing their job14. In times of an ongoing

debate about whether economic considerations should be prioritized over health15, there is

need for data to clarify the link between finances and mental health and their trajectories

during COVID-191,16.

We recently described a crowdsourcing platform (covid19resilience.org) that collected data

on COVID-19 related stress (worries) and mental health in population primarily composed of

US and Israel participants sampled during the acute pandemic outbreak17. In the current

research, we investigated the specific association between self-reported financial

consequences of COVID-19 and mental health (anxiety and depression) during the pandemic,

over and above the impact of health-related concerns. We further analyzed data from an

independent study conducted in Israel. In each study we investigated (1) cross sectional

associations between financial hardship and concurrent mental health and (2) mental health

trajectories over a month during the pandemic. We hypothesized that loss of income

(objective financial hardship) and perceptions of financial strain (the subjective feeling of

economic well being18) would be associated with increased anxiety and depression symptoms

and would predict the exacerbation of symptoms over time above and beyond health related



https://covid19resilience.org/

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COVID-19 concerns. We further tested whether financial stressors would be uniquely

associated with depression, above and beyond their effect on anxiety symptoms.

Methods

Study 1

Participants and procedure

Participants were ascertained through a crowdsourcing website (https://covid19resilience.org/)

that collected data on COVID-19 related stress (worries), resilience and mental health17. At

the end of the survey, participants received personalized feedback on their responses. The

feedback was meant to incentivize participants to complete the survey carefully. Participants

were offered the opportunity to leave their email address and be contacted for follow up

surveying. The study was advertised through, 1) the researchers’ social networks, including

emails to colleagues around the world; 2) social media; 3) the University of Pennsylvania and

Children’s Hospital of Philadelphia internal notifications and websites; and 4) organizational

mailing lists. The survey was available in English and Hebrew. The results presented are

based on data collected from April 6th to May 5th, 2020 for the baseline cross sectional

analysis (T1); Participants who left their email address in T1 and consented to be re-contacted

received an email inviting them to participate in a longitudinal survey between May 12th and

June 21st (T2). Participation required responders to provide online consent. The study was

approved by the Institutional Review Board of the University of Pennsylvania.

Measures

Anxiety and depression were measured using the Generalized Anxiety Disorder-7 (GAD-7)

questionnaire19 and Patient Health Questionnaire-2 (PHQ-2)20, respectively. COVID-19-

related worries (self-contracting COVID-19, family contracting COVID-19, financial burden

due to COVID-19) were measured on a 5-point Likert-type scale (from not at all to a great





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deal). Participants were also asked whether they had lost their job or whether their pay/hours

were reduced since the beginning of the outbreak (collapsed into a binary income loss

measure [yes/no]).

Statistical analysis

Cross sectional models

Linear mixed-effects models were applied to investigate associations of financial hardship

with mental health (anxiety and depression symptoms) as the dependent variable. In the first

model, we considered income loss (yes/no) as the independent variable. In the second model,

we considered the COVID-19 related financial strain (stress/worry) as the independent

variable, contrasted against the stress of self-contracting COVID-19, which served as the

reference variable for pandemic-related worries. This model also included the stress of a

family member contracting COVID-19. Symptom type (anxiety or depression) was addressed

as within-person repeated measure. Models included multiple potential confounders: age,

gender, pre-COVID-19 annual income (in USD (ordinal measure)), relationship status, living

alone, country of origin and date of survey completion.

Longitudinal models

For the longitudinal analysis we used linear mixed-effects models with the symptoms as the

key dependent variable, where change over time could be observed due to the longitudinal

nature of the data. Model 1 included dynamics (change) in income loss from T1 to T2.

Change in income loss consisted of four possible values (1-0, 0-0, 1-1, 0-1; 0=no loss;

1=loss), where income loss at T2 was considered as deterioration in financial situation (even

if already reported at T1, due to expected regression to the mean). Model 2 considered

change in worries between T1 and T2 and was calculated as the difference in responses

between time-points. Symptom type (anxiety or depression) was addressed as within-person



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repeated measure. Covariates were the same as cross-sectional models in addition to duration

between T1 and T2.

All analyses were performed using the lmerTest package21 in R.

Study 2

Participants and procedure

Participants were adult Israelis recruited through a survey link distributed among the

researchers’ social media network (WhatsApp contacts and in public groups on Facebook).

Data were collected between March 18th and 26th (T1), and again between April 22nd and May

7th (T2, for participants who agreed to be re-contacted for longitudinal surveying through

email). All surveys were conducted in Hebrew. Participation required providing online

consent. The study was approved by the Institutional Review Board of Sheba Medical Center.

Measures

We screened for anxiety and depression with the Hebrew version of the Patient-Reported

Outcomes Measurement Information System (PROMIS) Anxiety and Depression modules

22,23. COVID-19-related stressors (worries) were compiled from questions that have been

shown to be pertinent to mental health in previous research on the SARS and N1H1

pandemics24, and were measured on a 4-point Likert-type scale (From not at all to always).

Sociodemographic questions were the same as in study 1 with the exception of income (from

considerably below average to considerably above average) and income loss (from no income

loss to extreme income loss) that were measured on a 5-point Likert-type scale.

Statistical analysis

We employed similar models as described in study 1 with a few alterations: The key

dependent variables in the cross-sectional and longitudinal models were PROMIS scores



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(instead of GAD7/PHQ2). Income loss was on a 5-point scale (instead of binary in study 1),

therefore change in income loss was also calculated as the difference in responses between

T1 and T2.

Results

Study 1

Sample characteristics

Cross sectional data (T1) was collected from 5 717 participants who completed the resilience

survey and the COVID-19 related questions. Of them, 2 904 people completed GAD-7 and

PHQ-2 and were included in this study. Participants were mostly female (n = 2 259, 77·8%)

with mean age of 41·97 (SD = 13·55; range 18 to 91). 815 participants (28·1%) were in the

top income category (annual income>$150,000), 556 (19·0%) reported they experienced

income loss, and 223 (7·7%) endorsed the top category of financial worries due to COVID-

19. In the longitudinal survey (T2), data was obtained from 1 318 participants (54·8%

response rate out of n = 2 404 that were re-contacted after providing their email in T1).

Prevalence of females (n = 1 077, 81·7%), mean age (M = 40·79, SD = 13·59), income loss (n

= 246, 18·7%) and high financial strain (n = 99, 7·5%) resembled T1. Cohort characteristics

are presented in Table 1.

Cross sectional association of financial factors with mental health

Higher pre-COVID-19 income was negatively associated with overall anxiety and depression

(GAD7 and PHQ2) symptom load (β = -0.02, SE < 0.01, t (14 070) = -3.41, p < .001).

Income loss during COVID-19 (losing job or reduced pay) was associated with greater

depressive symptoms, but not anxiety (Figure 1A, income loss by symptom type



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(anxiety/depression) interaction, p < .0001) and had no main effect on general (anxiety and

depression) symptom load (p = .738).

COVID-19 related worries (self-contracting, family contracting and financial worries) were

associated with greater symptom load (β = 0.13, SE = 0.01, t (15 280) = 13.35, p < .0001).

Financial worries were specifically associated with depressive symptoms (Table 2, financial

worries by symptom type interaction, p < .0001).

Longitudinal association of 1-month dynamics in financial factors with mental health

trajectories

Income loss at T2 was associated with 1-month increase in general symptoms load (β = 0.26).

The increase in anxiety symptoms was steeper than that of depression symptoms (Figure 2A,

Table 3).

Increase in COVID-19 related worries between T1 and T2 was associated with an increase in

general symptoms load (β = 0.13, SE = 0.02, t (7 176) = 6.20, p < .0001). Increase in financial

worries was specifically associated with depressive symptoms (increase in financial worries

by symptom type interaction, p < .035). Full model statistics are summarized in Table 3.

Study 2

Sample characteristics

We conducted similar analysis on a replication cohort from Israel. Of the 1 376 participants

that participated in the survey, 1 267 people completed PROMIS and COVID-19 related

questions, and were included in this study. Participants were majority female (n = 687,

54·2%) with mean age of 35·21 (SD = 12·26). 142 participants (11·7%) were in the top

income category (reporting their income to be considerably above average), 501 (39·5%)

reported that they experienced income loss, and 226 (17·8%) endorsed the top category of



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financial worries due to COVID-19. In the longitudinal survey, data was obtained from 241

participants (49·7% response rate out of n = 485 that were re-contacted). Compared with T1,

the follow up sample included higher prevalence of females (n = 166, 68·9%) and lower

prevalence of people who endorsed the top category for COVID-19 related financial strain

(33, 13·7%). Age (M = 37·32, SD = 12·60) and rates of income loss (n = 102, 42·3%)

resembled T1. Cohort characteristics are presented in Table 1.

Cross sectional association of financial factors with mental health

Higher pre-COVID-19 income had no association with general symptoms (PROMIS

depression and anxiety) load (β = 0.02, SE = 0.02, t (2 192) = -1.37, p = .168). Income loss

during COVID-19 was associated with overall higher symptom load (Table 1, standardized

effect=.08, p < .0001). The association of income loss with depression was greater than the

association with anxiety (Figure 1B, income loss by symptom type interaction, p < .0001).

COVID-19 related worries (self-contracting, family contracting and financial worries) were

associated with greater symptoms load (β = 0.11, SE = 0.01, t (6 535) = 7.86, p < .0001).

Financial worries were specifically associated with depressive symptoms (Table 2, financial

worries by symptom type interaction, p < .0001).

Longitudinal (1-month) association of dynamics in financial factors with mental health

trajectories

A negative trend in financial situation (i.e., worsening of income loss by T2 compared to T1)

was associated with 1-month increase in depression (β = 0.07, p = .005), but had no

association with increase in general symptoms load (p = .817, Figure 2B, Table 3).

COVID-19 related worries had a trend-level effect on increase in general symptoms load (β =

0.07, p = .098). Increase in financial worries had no specific association with depressive



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symptoms (increase in financial worries by symptom type interaction, p = .741). Full model

statistics are summarized in Table 3.

Discussion

Across two independent studies, we found a specific link between financial factors and

depression, above and beyond anxiety, which was greater than the association between

health-related worries and depression. In cross-sectional analyses of data collected from over

4 000 participants, both loss of income and financial worries had unique effects on depression

in the two studies. In longitudinal analyses of ~1 500 participants, participants from both

cohorts who reported a negative trend in income loss reported an increase in depressive

symptoms over time. A specific effect of income loss and of financial worries on longitudinal

worsening of depressive symptoms, independent of anxiety, was observed in one of two

studies. Our findings are in line with data from previous global crises like the 2008 recession

that showed a link between financial factors and increase in depression25.

The abrupt loss of income (during the pandemic) was associated with overall more anxiety

and depressive symptoms, but was associated more strongly with depression than anxiety

symptoms, in both cohorts. This dissociation between anxiety and depression may indicate

that acute loss of income is a specific risk factor for depression during the pandemic. Our

results from Study 2 suggest that recent income loss (measured on a 5-point Likert scale)

contributes not only to initial depressive response (data collected at the onset of the

pandemic), but may also cause its amplification over time, with a 1-month exacerbation in

depressive symptoms associated with worsening in income loss. While we observed an

increase in depressive symptoms in Cohort 1 over time in association with income loss, this

effect was also observed with worsening of anxiety, even to a larger extent. It is possible that

demographic differences between the two cohorts may explain this difference in findings, as



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Cohort 1 was sampled from a wealthier population that was likely to have more “financial

buffering” capacity (i.e., savings, other financial resources beyond salary), and therefore the

impact on depression may not have been pronounced. It is also possible that the binary

measure that we had used in Cohort 1 to capture income loss (losing job/reduced pay) did not

allow sufficient variability in the data to specifically link the worsening of income loss with

depression. Our results are in line with recent COVID-19-related data12 and with previous

findings from economic crises, which associated unemployment and financial difficulties

(e.g., mortgage repayment difficulties, evictions, financial shortage ranging from difficulty in

paying loans to paying at the supermarket, etc.) with increased depression and help-seeking

behaviors9,25.

The link we observed between COVID-19 stress (worries) and depression was specific to

financial worries. Health-related worries about contracting COVID-19 or family contracting

it were associated with general symptoms load, but not depression specifically. This finding

controlled for pre-COVID-19 income, suggesting that the objective financial situation only

partly explains variability in depressive symptoms, and that worries about the financial

situation may be a sensitive marker (red flag) for depressive symptoms during the pandemic.

Coupled with the finding that objective income loss is tied to depressive symptoms and that

the results replicated across two independent cohorts, our data converge to support the

specific link between financial stressors and depressive symptoms. Previous works have

shown that financial strain - the subjective stress about financial concerns - is associated with

deterioration in mental health, even more than objective inability to meet financial

requirements18,26.

The urgent need for solid scientific data on the pandemic’s effect on health has spurred

concerns regarding validity and generalizability of COVID-19 related publications27. That

our cross-sectional results were replicated across two independent cohorts is a strength of the



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current study, and enables generalization of the findings beyond a local perspective. It also

mitigates the risk that our conclusions ensue from a type I error. The cohorts differed in terms

of demographics - Cohort 1 being multinational (primarily US and Israel), composed mainly

of highly educated people, enriched with healthcare providers and having a high prevalence

of women compared with Cohort 2, which had a larger proportion of students and young

adults, was more balanced in terms of gender distribution, and composed of Israeli population

exclusively. In addition, that the two studies used different measurements and still results

generally replicated across studies further supports the generalizability of our findings to

other populations globally and augurs well for the internal validity of the data.

In light of the urgent dilemma of lockdown versus reopening, with the tradeoff between

health and economy28, the results of this study may have several implications for

policymakers. We show here that the economic impact, which is strongly driven by lockdown

policies, may have significant mental health implications like increase in depression. Due to

the rising concerns regarding increased suicide risk in the face of the pandemic29, the possible

link between the financial impact of COVID-19 and depressive symptoms may be a red flag

for policy makers as they determine policies of reopening the economy. Thus, our study

emphasizes the importance of maintaining balance between necessary social distancing and

minimization of economic slowdown. In addition, our findings can guide clinicians as they

encounter patients during the pandemic, suggesting that healthcare providers should actively

probe patients for a change for the worse in their income, and ask them specifically about

their subjective stress regarding the financial impact of COVID-19. Our results may suggest

that these financial stressors are sensitive markers for the development of depression.

Importantly, our results suggest that the impact of income loss and financial strain on

depressive symptoms are independent of pre-COVID-19 income. We therefore suggest that

people from all backgrounds who report stress about their financial situation during the



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pandemic, including those with high income, are vulnerable to the effects of the financial

crisis on mental health, and therefore no one should be overlooked for their increased risk for

depression.

This study had several limitations. First, the sampling was not random, rather we employed a

“snowball” recruitment in both cohorts through the investigators’ social networks. Therefore,

potential biases that were reported recently in online surveys during COVID-19 should be

considered27. Second, we had substantial attrition between T1 and T2 in both studies,

especially in Cohort 2, where we were likely underpowered to test longitudinal trajectories.

Therefore, a type II error is possible. Third, in Cohort 1, the measure for income loss was

binary, which limited us in assessing variability in the trajectory over time. Lastly, our study

employed online crowdsourcing data collection, with all of its inherent limitations as

participants were not interviewed in person30. Still, arguably even when accounting for the

limitations mentioned above, that overall the association between objective (income loss) and

subjective (financial worries) financial factors with depressive symptoms was replicated in

two independent studies mitigates most concerns regarding generalizability of our findings.

To conclude, we provide converging evidence to suggest a specific association between

financial stressors and depression during a global pandemic. A decrease in income during this

time, as well as perceived financial strain, can lead to deterioration in mental health and

might generate depression in a specific manner. Our findings may suggest that the “financial

COVID-19” could have a serious impact on health, specifically mental health, which should

be gravely considered by policy makers as they decide steps resulting in economic slowdown

due to COVID-19 specific physical health concerns. In light of the growing concern about

increase in suicide following the pandemic29, our findings provide empirical data collected

during the pandemic that may help the efforts to improve early detection of and intervention

with people at increased risk for depression. Future research could focus on early



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interventions that will enable mental health services to offer timely help to people who were

financially harmed by the pandemic.



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Statements

Acknowledgment

We thank participants of covid19resilience.org for their contribution to data generation. We

wish to thank Amit Lewinthal, Shira Bursztyn and Dana Basel for their assistance in

collecting the data. Special acknowledgment to Prof. Avner De-Shalit from the Hebrew

University of Jerusalem, for his consultancy and enlightening insights.

Funding Sources

This study was supported by the National Institute of Mental Health (NIMH) grants K23-

MH120437 (RB), R01-MH119219 (REG, RCG), R01-MH117014 (RCG), the US-Israel

Binational Science Foundation Grant No. 2017369 (REG, DG and RB), Foundation Dora,

Kirsh Foundation and the Lifespan Brain Institute of Children’s Hospital of Philadelphia and

Penn Medicine, University of Pennsylvania. DMG was funded in part by the Zuckerman

STEM Leadership Program. The funding source had no role in the study design, collection,

analysis, or interpretation of data, the writing of the article, or decision to submit the article

for publication.

Conflict of Interest

RB serves on the scientific board and reports stock ownership in ‘Taliaz Health’, with no

conflict of interest relevant to this work. All other authors have no conflicts of interest do

declare.

Statement of Ethics

The study was approved by the Institutional Review Boards of the University of

Pennsylvania and Sheba Medical Center.



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Study protocols and data sharing

Data collected for this study includes individual participants’ data. Data cannot be publicly

accessible due to Institutional Review Board guidelines. We are open to collaborations with

other researchers upon contacting us.

Author Contributions

All authors significantly contributed to, reviewed and approved the final manuscript.

Conceiving and designing the study: NHP, TMM, DG, ID, DMG, LAB, EV, LKW,MHH,

MLS, RG, RCG, REG, IMP, RB

Data collection: NHP, GED, NM, RB

Statistical analyses: TMM

Data interpretation: NHP, TMM, DG, GED, ID, DMG, LAB, NM, LKW, EV, MHH, MLS,

RG, RCG, REG, IMP, RB

Writing the final manuscript: NHP, TMM, DG, IMP, RB



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Table 1- Cohorts’ characteristics

Cohort 1 (Penn) Cohort 2 (Sheba)

T1 d

ata

Dem

ogr

aph

ics

N 2 904 1 267

Data collected between April 6th – May 5th,

2020 March 18th – 26th, 2020

US/Israel 2 111 / 597 0 / 1 267

Age, mean in years (SD) 41·97 (13·55) 35·21 (12·26)

Female, n (%) 2 259 (77·8%) 687 (54·2%)

Married/living with partner, n (%)

1 976 (68·0%) 768 (60·6%)

Living alone during COVID-19, n (%)

546 (18·8%) 404 (31·9%)

CO

VID

-19

rel

ated

stre

ss

Pre-COVID-19 income, measure

Annual income brackets in $

Considerably below (1) – considerably above (5)

average Top income category, n (%) 815 (28·1%) 148 (11·7%)

COVID-19 related stress, measure

Not at all (1) – Great deal (5)

Not at all (1) – Always (4)

Contracting COVID-19, top category, n (%)

226 (7·8%) 52 (4·1%)

Family contracting COVID-19, top category, n (%)

641 (22·1%) 230 (18·2%)

Fin

anci

al

mea

sure

s

Financial strain due to COVID-19, top category, n (%)

223 (7·7%) 226 (17·8%)

Income loss, measure Lost job / Reduced

pay / Reduced hours 5-point Likert scale

Income loss, n (%) 556 (19·0%) 501 (39·5%)

T2 d

ata

N 1 318 241

Dem

ogr

aph

ics

Data collected between May 12th – June 6th April 22nd – May 7th

Days from T1 to T2, mean (SD)

31·6 (5·4) 37·0 (4·0)

US/Israel 1 028 / 210 0 / 241

Age, mean in years (SD) 40·79 (13·59) 37·72 (12·6)

Female, n (%) 1 077 (81·7%) 166 (68·9%)

Fin

anci

al

mea

sure

s

Income loss, n (%) 246 (18·7%) 102 (42·3%)

Financial strain due to COVID-19, top category, n (%)

99 (7·5%) 33 (13·7%)



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Table 2- Cross-sectional associations of financial hardship with mental health

Values were derived from linear mixed-effects models with financial hardship (objective in Model 1;

subjective in Model 2) as independent variables, and standardized symptoms load (standardized PHQ-

2 + GAD-7 scores in Cohort 1, PROMIS Anxiety and Depression scores in Cohort 2) as dependent

variable. Models are adjusted for age, sex, relationship status, income and country of origin. Effect

sizes are represented with standardized effect coefficients (β). SE= Standard error.

#Modeled as the interaction between income loss and type of symptom domain (anxiety/depression). @Modeled as the interaction between worry type (family contracting COVID-19/financial concern

about impact of COVID-19, contrasted against worries to self-contract COVID-19 as reference) and

type of symptom domain (anxiety/depression).

Cohort 1 Cohort 2

Standardized β

(SE) t p

Standardized β

(SE) t p

Model 1: Objective financial hardship

Income- main effect on symptoms

load (anxiety and depression) -0·02 (0·01) -3·41 <0·001 -0·02 (0·02) -1·37 0·168

Income loss- main effect on

symptoms load (anxiety and

depression)

0·01 (0·04) 0·33 0·738 0·08 (0·02) 4·90 <0·0001

Specific effect of income loss on

depression# 0·14 (0·02) 8·19 <0·0001 0·06 (0·01) 8·75 <0·0001

Model 2: Subjective financial strain

Overall COVID-19 worries- main

effect on symptoms load (anxiety

and depression)

0·13 (0·01) 13·35 <0·0001 0·11 (0·01) 7·86 <0·0001

Worries about family contracting

COVID-19- specific effect on

Depression@

-0·01 (0·01) -1·03 0·304 -0·02 (0·02) -1·19 0·232

Worries about finance- specific

effect on Depression@ 0·17 (0·02) 10·35 <0·0001 0·15 (0·02) 6·08 <0·0001



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Table 3- Longitudinal (1-month) association of financial hardship with trajectories of mental

health

Values were derived from linear mixed-effects models with financial hardship (objective in Model 1;

subjective in Model 2) as independent variables, and standardized symptoms load (standardized PHQ-

2 + GAD-7 scores in Cohort 1, PROMIS Anxiety and Depression scores in Cohort 2) as dependent

variable. Models are adjusted for age, sex, relationship status, income and country of origin. Effect

sizes are represented with standardized effect coefficients (β). SD= Standard deviation. SE= Standard

error. #Modeled as the interaction between trajectory of income loss and change in type of symptom domain

(anxiety/depression). @Modeled as the interaction between trajectory of change in worry type (family contracting COVID-

19/financial concern about impact of COVID-19, contrasted against worries to contract COVID-19)

and type of symptom domain (anxiety/depression).

Cohort 1

Mean (SD) days from T1: 31·6 (5·4)

Cohort 2

Mean (SD) days from T1: 37·0 (4·0)

Standardized β

(SE) t p

Standardized β

(SE) t p

Model 1: Trajectory of objective financial hardship

Income loss trajectory- main effect on increase in symptoms load (anxiety and depression)

0·26 (0·02) 14·66 <0·0001 0·01 (0·05) 0·23 0·817

Income loss trajectory- specific effect on increase in

depression#

-0·07 (0·02) -4·89 <0·0001 0·07 (0·03) 2·76 0·005

Model 2: Trajectory of subjective financial strain

Worries trajectory- main effect on increase in symptoms load (anxiety and depression)

0·13 (0·02) 6·20 <0·0001 0·07 (0·04) 1·65 0·098

Worries about family contracting COVID-19 trajectory- specific effect on

increase in depression@

0·05 (0·04) 1·06 0·285 0·01 (0·09) -0·03 0·975

Worries about finance trajectory- specific effect on

increase in depression@

0·07 (0·03) 2·10 0·035 0·02 (0·08) 0·32 0·741



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Figure 1- Cross sectional associations between objective hardship (income loss) with anxiety

and depression.

Caption. (A) Associations in Cohort 1; 1 represents losing job\reduced pay\reduced hours. (B)

associations in Cohort 2; x-axis represents income loss in a 5-point Likert scale from not at all [1] to

extreme income loss [5]. y-axis is standardized (z) symptom score/load.



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Figure 2- Associations between one-month trajectories of objective financial hardship

(income loss) and anxiety and depressive symptoms in longitudinal assessment.

Caption. (A) Cohort 1 and (B) Cohort 2. X- axis represents change in income loss from T1 to T2,

regressed for T1. Negative values on the x-axis indicate that participants reported income loss the first

time (T1), and then by the second time (T2), they reported milder income loss. y-axis is standardized

(z) symptom load.



https://doi.org/10.1101/2020.09.15.20195339


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